Detecting Embankment Instability Using Measurable Track Geometry Data

The British railway system is the oldest in the world. Most railway embankments are aged around 150 years old and the percentage of disruption reports that feature them is frequently higher than other types of railway infrastructure. Remarkable works have been done to understand embankment deteriora...

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Bibliographic Details
Main Authors: David Kite, Giulia Siino, Matthew Audley
Format: Article
Language:English
Published: MDPI AG 2020-03-01
Series:Infrastructures
Subjects:
Online Access:https://www.mdpi.com/2412-3811/5/3/29
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spelling doaj-6e6b8fa7dcf144bf8237cecf6c42ed602020-11-25T02:01:59ZengMDPI AGInfrastructures2412-38112020-03-01532910.3390/infrastructures5030029infrastructures5030029Detecting Embankment Instability Using Measurable Track Geometry DataDavid Kite0Giulia Siino1Matthew Audley2Asset Research Consultancy Department, AECOM, Nottingham NG9 6RZ, UKAsset Research Consultancy Department, AECOM, Nottingham NG9 6RZ, UKAsset Research Consultancy Department, AECOM, Nottingham NG9 6RZ, UKThe British railway system is the oldest in the world. Most railway embankments are aged around 150 years old and the percentage of disruption reports that feature them is frequently higher than other types of railway infrastructure. Remarkable works have been done to understand embankment deterioration and develop asset modelling. Nevertheless, they do not represent a sufficient way of managing assets in detail. As a result, reactive approaches combined with proactive ones would improve the whole asset management scenario. To guarantee good system performance, geotechnical asset management (GAM) aims to reduce uncertainty through informed, data driven decisions and optimisation of resources. GAM approaches are cost sensitive. Thus, data driven approaches that utilize existing resources are highly prized. Track geometry data has been routinely collected by Network Rail, over many years, to identify track defects and subsequently plan track maintenance interventions. Additionally, in 2018 Network Rail commissioned AECOM to undertake a study, described in this paper, to investigate the use of track geometry data in the detection of embankment instabilities. In this study, track geometry data for over 1800 embankments were processed and parameters offering the best correlation with embankment movements were identified and used by an algorithm to generate an embankment instability metric. The study successfully demonstrated that the instability of railway embankments is clearly visible in track geometry data and the metric gives an indication of the worsening of track geometry, that is likely due to embankment instability.https://www.mdpi.com/2412-3811/5/3/29smart infrastructuresustainabilityresilienceland use optimisationtransportgeotechnical asset managementembankment degradationrailway track degradationmaintenance
collection DOAJ
language English
format Article
sources DOAJ
author David Kite
Giulia Siino
Matthew Audley
spellingShingle David Kite
Giulia Siino
Matthew Audley
Detecting Embankment Instability Using Measurable Track Geometry Data
Infrastructures
smart infrastructure
sustainability
resilience
land use optimisation
transport
geotechnical asset management
embankment degradation
railway track degradation
maintenance
author_facet David Kite
Giulia Siino
Matthew Audley
author_sort David Kite
title Detecting Embankment Instability Using Measurable Track Geometry Data
title_short Detecting Embankment Instability Using Measurable Track Geometry Data
title_full Detecting Embankment Instability Using Measurable Track Geometry Data
title_fullStr Detecting Embankment Instability Using Measurable Track Geometry Data
title_full_unstemmed Detecting Embankment Instability Using Measurable Track Geometry Data
title_sort detecting embankment instability using measurable track geometry data
publisher MDPI AG
series Infrastructures
issn 2412-3811
publishDate 2020-03-01
description The British railway system is the oldest in the world. Most railway embankments are aged around 150 years old and the percentage of disruption reports that feature them is frequently higher than other types of railway infrastructure. Remarkable works have been done to understand embankment deterioration and develop asset modelling. Nevertheless, they do not represent a sufficient way of managing assets in detail. As a result, reactive approaches combined with proactive ones would improve the whole asset management scenario. To guarantee good system performance, geotechnical asset management (GAM) aims to reduce uncertainty through informed, data driven decisions and optimisation of resources. GAM approaches are cost sensitive. Thus, data driven approaches that utilize existing resources are highly prized. Track geometry data has been routinely collected by Network Rail, over many years, to identify track defects and subsequently plan track maintenance interventions. Additionally, in 2018 Network Rail commissioned AECOM to undertake a study, described in this paper, to investigate the use of track geometry data in the detection of embankment instabilities. In this study, track geometry data for over 1800 embankments were processed and parameters offering the best correlation with embankment movements were identified and used by an algorithm to generate an embankment instability metric. The study successfully demonstrated that the instability of railway embankments is clearly visible in track geometry data and the metric gives an indication of the worsening of track geometry, that is likely due to embankment instability.
topic smart infrastructure
sustainability
resilience
land use optimisation
transport
geotechnical asset management
embankment degradation
railway track degradation
maintenance
url https://www.mdpi.com/2412-3811/5/3/29
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AT giuliasiino detectingembankmentinstabilityusingmeasurabletrackgeometrydata
AT matthewaudley detectingembankmentinstabilityusingmeasurabletrackgeometrydata
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